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    "text": "JADS: Renato Calzone\nLaNubia NL: Rigo Selassa\nLaNubia AM: Aldo Silvano"
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    "text": "Friday, July 29, 2022\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSunday, March 20, 2022\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFriday, October 15, 2021\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSaturday, May 15, 2021\n\n\n\n\n\n\nNo matching items"
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    "text": "Achieving a proper balance between human operators and AI algorithms (hybrid AI),\nDeveloping forecasting algorithms that are both accurate and reliable,\nRealizing predictive maintenance involving time-to-failure predictions,\nDeveloping AI for power grid balancing using recommendation enhanced Demand Response, and\nUnderstanding and advancing the readiness to accept the use of AI both within the critical infrastructure and the general population.\n\n\nProject 1\n\nArtificial Intelligence for decision support in water desalination, recycling, and purification\n\n\nScientific challenge:\n\n\nFor over 30 years, AI and computational intelligence have been used in water desalination domain (He et al., 2022) for applications like support in the decision making, prediction, optimization, and control with respect to alarm processing, fault detection, load forecasting, and security assessment.\n\n\nClick to Read More!\n\nThe recent trend to use renewable energy sources for desalination and wastewater treatment makes the decision process more complicated, due to the temporal variability of these sources (Cabrera and Carta, 2019; Harrou et al., 2018; Cheng et al, 2020). AI systems that go beyond the current point solutions are needed to deal with this complexity, while considering a system-wide view and a balanced interaction between the AI systems and human experts. The scientific challenge of this PhD project is to develop hybrid AI solutions that combine advanced prediction methods (e.g. deep learning algorithms), multi-objective optimization and adaptive models with explainable AI decision-making methods from computational intelligence to realize such a balance.\n\n\n\n\n\n\n\nPhD promoters: Dr. Laura Genga and Prof.Dr.Ir. Uzay Kaymak\n\n\n\n\nProject 2\n\nArtificial Intelligence for power load and renewable energy forecasting in electricity grids\n\n\nScientific challenge\n\n\nShort-term forecasts of (a) power load and (b) renewable energy supply, are crucial for decarbonising electricity grids: without these forecasts, high-carbon baseload generators must be kept running.\n\n\nClick to Read More!\n\nThe scientific challenge is to achieve accurate and reliable forecasts, in the face of changeable energy demand patterns and external covariates (weather, public events, etc). Deep learning has been shown to perform very well on power-load forecasting and achieves promising results on renewable-energy forecasting (Wang et al., 2019). This PhD plan sets out to develop deep learning algorithms that realize forecasts that are both accurate and reliable, with a flexibility to adapt to local conditions.\n\n\n\n\n\n\n\nPhD promoters: Dr. Dan Stowell and Dr. Ciçek Güven\n\n\n\n\nProject 3\n\nArtificial Intelligence for predictive maintenance in water and electricity infrastructure\n\n\nScientific challenge\n\n\nPredictive maintenance offers great potential value to the energy and water supply industry (cf. SDG7). Timely detection of required maintenance of machines, sensors, or other critical infrastructure can prevent disruptions of service and costly loss of resources.\n\n\nClick to Read More!\n\nFor instance, visual sensors can be used to analyze and detect subtle patterns (Hendrix et al. 2021; van Lieshout, van Oeveren, van Emmerik, & Postma, 2020; Noord and Postma, 2017) and auditory sensors can pick up subtle changes in sounds (Buisman & Postma, 2012). More generally, artificial intelligence offers improved prediction performance on predictive maintenance tasks. Recent advances in visual object recognition and auditory analysis allow for a continuous and reliable monitoring of system states. In particular, the focus will be on self-supervised and unsupervised learning (see e.g. Olier et al., 2018). In the absence of supervisory labels, adequate priors will be acquired using large unlabeled datasets (see e.g. Ding et al., 2022). In the context of Industry 4.0, predictive maintenance leads to numerous innovations. One of the main challenges is to deal with real time-based predictive maintenance (Zonta et al., 2020). Instead of treating predictive maintenance as a simple alert monitoring, real time-based predictive maintenance offers an estimate of time-to-failure.\n\n\n\n\n\n\n\nPhD promoters: Dr. Sebastian Olier and Prof. Dr. E.O. Postma\n\n\n\n\nProject 4\n\nAI for power grid balancing using recommendation-enhanced Demand Response\n\n\nScientific challenge\n\n\nTo improve grid balancing (SDG7), esp. in case of many renewable energy resources and fluctuating demand, AI forecasting methods (WP2) can be combined with Demand Response (DR) methods. DR motivates energy consumers in some way (e.g. pricing-based) to adjust their energy usage to the available energy resources and demand.\n\n\nClick to Read More!\n\nSmart grid technology allows DR to be more data-driven and a multitude of AI technologies have already been applied to DR (Antonopoulos et al., 2020), including ML, deep learning and agent-based approaches. However, little research has investigated the consumer side of DR apart from simple customer segmentation approaches (Antonopoulos et al., 2020). Rather than having consumers (household or industry) passively follow the DR (pricing) scheme, AI technology such as recommender algorithms could play an active role in recommending consumers how and when to distribute their energy usage and return based energy forecasts and DR information. Such tailored interventions to improve DR approaches and optimize dynamic grid balancing.\n\n\n\n\n\n\n\nPhD promoters: Dr.Ir. Martijn Willemsen and Dr. Claudia Zucca\n\n\n\n\nProject 5\n\nSocial support for the real-world introduction of AI in critical infrastructure\n\n\nScientific challenge\n\n\nThe social impact of technology on its users has been vastly proved to be enormous (King and He, 2006) since it might pose organizational and social obstacles. Acceptance of new technologies, especially in the energy field, has been recognized as one of the primary barriers to implementing technological innovations (Huijts, Molin, and Steg, 2012).\n\n\nClick to Read More!\n\nIn the context of the ILUSTRE project, the impact of IT technology on the energy and water supply domains is twofold. First: the impact on the partners industry managers and employees. The implementation creates disruption, and unless managers support the innovation and workers understand and comply with the new infrastructure, they might actively oppose the implementation. Second: the impact on the broader society since AI technology will affect the quality and the features of the services provided to the population. The scientific challenge addressed in this Ph.D.-project is to employ group model building (GMB) and social network analysis (SNA) to monitor the extent to which the employees and the larger public (together called the stakeholders) receive and respond to the implementation. GMB is a widely used approach to collect data and monitor (and influence) the opinions and sentiments of groups of stakeholders (Peck, 1998). SNA has been used extensively to analyze the group dynamics at the roots of technological implementation reception (Sasovova and Leenders, 2009). These techniques can well be used in conjunction with agent-based simulation models.\n\n\n\n\n\n\n\nPhD promoters: Dr. Claudia Zucca and prof. Dr. Roger Leenders"
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    "text": "ILUSTRE stands for Innovation Lab for Utilities on Sustainable Technology and Renewable Energy and is one of the 17 Innovation Centers for Artificial Intelligence (ICAI) within the ROBUST Long-Term Programme (LTP).The ROBUST LTP is a consortium of knowledge institutes and industrial stakeholders collaborating on multi-year academic research into reliable artificial intelligence and focusing on the translation of the research results into trustworthy technologies and processes to support and improve interactions between people and systems.\nBased on the lovely Island of Curaçao, ILUSTRE will focus on long-term academical research and talent development to stimulate, support and facilitate the energy transition in the Caribbean and Latin American region and make significant steps in attaining the United Nations Sustainable Development Goals 6 (Clean Water and Sanitation) and 7 (Affordable and Clean Energy)."
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    "text": "Order By\n       Default\n         \n          Date - Oldest\n        \n         \n          Date - Newest\n        \n         \n          Title\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\n\nDate\n\n\nTitle\n\n\n\n\n\n\n\n\nFriday, July 29, 2022\n\n\nPhD in AI for Water Desalination, recycling and purification\n\n\n\n\n\n\n\nFriday, July 29, 2022\n\n\nPhD in AI for predictive maintenance in water and electricity infrastructure\n\n\n\n\n\n\n\nFriday, July 29, 2022\n\n\nPhD in AI for power load and renewable energy forecasting in electricity grids\n\n\n\n\n\n\n\nMonday, August 1, 2022\n\n\nPhD in Social support for real world introduction of AI in critical infrastructure\n\n\n\n\n\n\n\nMonday, August 1, 2022\n\n\nPhD in AI for power grid balancing using recommenendation-enhanced Demand response\n\n\n\n\n\n\n\n\n\nNo matching items"
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    "text": "Job Description\nAs part of the Ilustre lab, you will develop recommendation technologies to tailor advice on energy usage decisions in the smart grid. Demand-Response methods motivates energy consumers in some way (e.g. pricing-based) to adjust their energy usage to the available energy resources and demand. Smart grid technology allows DR to be more data-driven and a multitude of AI technologies have already been applied to DR but the consumer side of has not been developed much. However, modeling and supporting the energy decisions of users is crucial, as a smart grid will only be successful if the users of the systems behave predictable and in the most energy efficient way. For example, charging your electric car when the solar PV generated in the neighborhood is highest will put the smallest load on the grid and will be cheapest, but can only be achieved if it fits in the users’ calendar and daily habits.\nIn this project you will extend current AI energy consumption forecasting and DR methods with tailored and explainable recommendations that will provide both household and industry consumers with tailored advice how to adjust for Demand Response. As many stakeholders are involved, and dynamic changes in energy usage will affect other consumers in the network, a multi-stakeholder perspective on recommendations should be employed. You will work together with other PhD students in the lab that will work on forecasting and on identifying the stakeholders.\nIn the project you will review existing methods for AI in DR, work on recommender model and approaches, test several of these models in prototypes and in a pilot project within the Ilustre lab.\n\n\nJob Requirements\n\nA master’s degree (or an equivalent university degree) in computer science, data science, AI or related, with a strong background in machine learning and recommender systems. Affinity with human-computer interaction methods and interest in human cognition and decision making is a plus.\nA research-oriented attitude.\nAbility to work in a team and interest in collaborating with the industrial partners.\nAbility to communicate and translate the research results to practice.\nAcademic curiosity and an interest to collaborate in a multidisciplinary context.\nFluent in spoken and written English (C1 level).\n\n\n\nConditions of Employment\nBeing appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.\nAs a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.\nTo develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.\nNext to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).\nJADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.\nAll researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.\n\n\nMore Information\nDo you recognize yourself in this profile and would you like to know more? Please contact Dr. Martijn Willemsen or Dr. Claudia Zucca. For information about terms of employment, please contact Marielle van Gerven, HR Advisor or call\n\n\nApplication\nWe invite you to submit a complete application by using the ‘apply now’-button on this page. The application should include a:\n\nCover letter in which you describe your motivation and qualifications for the position.\nCurriculum vitae, including a list of your publications and the contact information of three references.\nBrief description of your MSc thesis.\n\nWe look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.\nApply Now"
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    "text": "Job Description\nIn this position, you will be collecting data using group model building (GMB) and survey methodology. Then you will employ social network analysis (SNA) to monitor the extent to which the employees and the larger public (together called the stakeholders) receive and respond to the implementation. These techniques will be used in conjunction with system dynamics and agent-based simulation models.\n\n\nJob Requirements\n\nA master’s degree in Computational Social Science, Statistics, Computer Science, Data Science, Sociology, Political Science, Organizational Studies with a strong quantitative background, or related disciplines.\nPreferably experience with Social Network Analysis, Data collection techniques, System Dynamics Modelling, Agent Based Modelling.\nStrong programming skills, preferably in R.\nA research-oriented attitude and interest in collaborating with the industrial partners\nExperience in dealing with stakeholders.\nFluent in spoken and written English (C1 level).\n\n\n\nConditions of Employment\nBeing appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.\nAs a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.\nTo develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.\nNext to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).\nJADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.\nAll researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.\n\n\nMore Information\nDo you recognize yourself in this profile and would you like to know more? Please contact Prof. Dr. Roger Leenders or Dr. Claudia Zucca. For information about terms of employment, please contact Marielle van Gerven, HR Advisor or call\n\n\nApplication\nWe invite you to submit a complete application by using the ‘apply now’-button on this page. The application should include a:\n\nCover letter in which you describe your motivation and qualifications for the position.\nCurriculum vitae, including a list of your publications and the contact information of three references.\nBrief description of your MSc thesis.\n\nWe look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.\nApply Now"
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    "text": "Job Description\nThis position is linked to ILUSTRE, a new Innovation Center for Artificial Intelligence (ICAI) to be established in Curaçao. ILUSTRE will be a living lab in the Caribbean with the objective to develop, implement and test AI innovations that will accelerate the use of clean energy and advanced solutions in water treatment and wastewater recycling/purification. The innovation lab is one of the new ICAI labs that are part of the ROBUST program on Trustworthy AI-based Systems for Sustainable Growth which is financed under the NWO LTP funding scheme. ILUSTRE offers opportunities for PhD candidates who wish to support to the acceleration of the energy transition and who have an affinity with the Dutch Caribbean islands.\n\n\nJob Requirements\n\nEssential:\n\nA master’s degree (or an equivalent university degree) in Computer Science, Data Science, Artificial Intelligence, Electrical Engineering, or a closely related quantitative subject.\nA research-oriented attitude.\nAbility to work in a team\nInterest in collaborating with the industrial partners.\nFluent in spoken and written English (C1 level).\n\nDesirable:\n\nExperience of programming to implement machine learning or statistics\n\n\n\n\nConditions of Employment\nBeing appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.\nAs a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.\nTo develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.\nNext to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).\nJADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.\nAll researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.\n\n\nMore Information\nDo you recognize yourself in this profile and would you like to know more? Please contact Dr. Dan Stowell or Dr. Çiçek Güven. For information about terms of employment, please contact Marielle van Gerven, HR Advisor or call\n\n\nApplication\nWe invite you to submit a complete application by using the ‘apply now’-button on this page. The application should include a:\n\nCover letter in which you describe your motivation and qualifications for the position.\nCurriculum vitae, including a list of your publications and the contact information of three references.\nBrief description of your MSc thesis.\n\nWe look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.\nApply Now"
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    "text": "Job Description\nClean water and sanitation is one of the 17 Sustainable Development Goals of United Nations. Reaching these goals in an energy-efficient, environmentally friendly and sustainable way requires optimization and balancing of multiple decisions in a complex setting. For example, the recent trend to use renewable energy sources for desalination and wastewater treatment makes the decision process more complicated, due to the temporal variability of these sources. To deal with this complexity, AI systems that go beyond the current point solutions are needed, while also considering the broader perspective and a balanced interaction between the computerized systems and human experts, providing them with understandable explanations on the inner working of the AI system. The scientific challenge of this PhD project is to develop hybrid AI solutions that combine advanced prediction methods (e.g. deep learning algorithms), multi-objective optimization and adaptive models with explainable AI decision-making methods from computational intelligence to realize such a balance. As the PhD working on this challenge, you will become part of the Computational Intelligence for Decision Support Lab at JADS and will collaborate with other PhDs working Data Analytics Unit. You will also contribute to the Trustworthy AI-based Systems for Sustainable Growth Lab (ILUSTRE) in which this project is embedded. In this diverse, inclusive, and interdisciplinary environment, you are expected to collaborate closely with our social and industrial partners from the Netherlands and Curaçao.\n\n\nJob Requirements\n\nA master’s degree (or an equivalent university degree) in artificial intelligence, data science, computer science or an equivalent quantitative field.\nA research-oriented attitude.\nAbility to work in a team while also taking a pro-active attitude to drive your own research in collaboration with industrial partners and other stakeholders.\nFluent in spoken and written English (C1 level).\n\n\n\nConditions of Employment\nBeing appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.\nAs a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.\nTo develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.\nNext to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).\nJADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.\nAll researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.\n\n\nMore Information\nDo you recognize yourself in this profile and would you like to know more? Please contact Prof. Dr. Rr. U. Kaymak or call. For information about terms of employment, please contact Marielle van Gerven, HR Advisor or call\n\n\nApplication\nWe invite you to submit a complete application by using the ‘apply now’-button on this page. The application should include a:\n\nCover letter in which you describe your motivation and qualifications for the position.\nCurriculum vitae, including a list of your publications and the contact information of three references.\nBrief description of your MSc thesis.\n\nWe look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.\nApply Now"
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    "text": "Job Description\nPredictive maintenance offers great potential value to the energy and water supply industry. Timely detection of required maintenance of machines, sensors, or other critical infrastructure can prevent service disruptions and costly loss of resources. For instance, visual sensors can be used to analyze and detect subtle patterns, and auditory sensors can pick up subtle sound changes.\nArtificial intelligence allows for improved prediction performance on predictive maintenance tasks. However, one of the main challenges is dealing with real-time-based predictive maintenance. Instead of treating predictive maintenance as a simple alert monitoring, real-time-based predictive maintenance offers an estimate of time-to-failure. You will be addressing this challenge by working with large amounts of unlabeled visual and auditory datasets to create models for continuous and reliable monitoring of system states that can accurately predict time-to-failure.\nYou will work on deep learning solutions to achieve real-time-based predictive maintenance algorithms. These models will be based on unsupervised and self-supervised learning, and you will work with both discriminative and generative models. Moreover, you will study the accuracy, reliability, and possible integration of these models with water and energy providers and other industrial partners.\nYour work will contribute to achieving a more reliable distribution of energy and water and to the more general goal of building robust machine learning for a sustainable society.\nYou will be part of the Jheronimus Academy of Data Science in ’s-Hertogenbosch and closely collaborate with Tilburg University and the Eindhoven University of Technology.\n\n\nJob Requirements\nWe are looking for candidates that meet the following criteria: - Master’s degree in a relevant field - e.g. Artificial Intelligence, Computer Science, Engineering, Cognitive Science. - Good knowledge of the theory and application of machine learning, particularly deep learning - demonstrable experience with self-supervised learning or deep generative models is a plus. - Well-developed programming skills - experience with deep learning frameworks is expected. - Good communicative skills in English, both in speaking and in writing (C1 level).\n\n\nConditions of Employment\nBeing appointed at JADS provides a meaningful job in a dynamic and ambitious community of two universities and startups.\nAs a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.\nTo develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.\nNext to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).\nJADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.\nAll researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.\n\n\nMore Information\nDo you recognize yourself in this profile and would you like to know more? Please contact Dr. Juan Sebastian Olier) or Prof. Dr. Eric Postma. For information about terms of employment, please contact Marielle van Gerven, HR Advisor or call\n\n\nApplication\nWe invite you to submit a complete application by using the ‘apply now’-button on this page. The application should include a:\n\nCover letter in which you describe your motivation and qualifications for the position.\nCurriculum vitae, including a list of your publications and the contact information of three references.\nBrief description of your MSc thesis.\n\nWe look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.\nApply Now"
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    "text": "We were warmly welcomed by Minister Ruisandro Cijntje of MEO and his staff, Director of Foreign Economic Cooperation Vanessa Toré and Policy Director Luelo Girigorie. Aqualectra CFO Neysa Isenia and CIO Julius Griffith welcomed us at their site in Willemstad.\nFor more details please read the article here or the linkedin post."
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    "text": "We organized a session on Diaspora for Export: ILUSTRE Innovation Lab for Utilities on Sustainable Technology and Renewable Energy. Read more here.!"
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    "text": "The objective of this lab: to develop, implement and test AI innovations that will accelerate the use of clean energy and advance solutions in water treatment and wastewater recycling/purification.\nClick for more details and joining."
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    "title": "Introduction ILUSTRE at JADS Oct 2021",
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    "text": "Next to this all we spoke to our main guests, and received other guests: from ICAI Esther Smits joined us online and in the studio Noelle Cicilia (JADS & LUX Data), Renato Calzone (JADS), and Winifred Andriessen (KPN) were also be present.\nEvent details"
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    "text": "Academic Partners\n\n\nJADS\n\nJADS is a unique cooperation between the Province of North Brabant, the Municipality of ’s-Hertogenbosch, Tilburg University and Eindhoven University of Technology (TU/e). They offer various data science programs.At JADS, researchers and students work closely with the business community. In addition to education and research, JADS also offers space for innovative, data-driven entrepreneurship and public-private partnerships.\n\n\n\n\n\n\n\n\nUOC\n\nOffers academic and professional higher education in a broad variety of disciplines, the University of Curaçao focuses mainly on applied research, with a practical approach, meaning that real-life practice is an important component in our academic programs.\n\n\n\n\n\n\n\n\nIndustry Partners\n\n\nLaNubia\n\nLaNubia Consulting was founded in 2014 by Deloitte and RSM OneMBA Alumni Rigo Selassa. With his passion to inspire clients to transform using technological innovations, Rigo Selassa is building a team of Professionals to guide Business Transformation initiatives worldwide.\n\n\n\n\n\n\n\n\nAqualectra\n\nAqualectra is Curacao’s government owned utilities company that produces and distributes water and electricity to over 80.000 households and companies. Aqualectra employs 615 dedicated women and men who provide the framework for the delivery of quality products and services to our customers.\n\n\n\n\n\n\n\n\nWEB Bonaire\n\nWater- en Energiebedrijf Bonaire N.V. (WEB) was founded in 1963 and is owned by the Public Entity of Bonaire (OLB). WEB is responsible for the sustainable, reliable and affordable supply of drinking water and electricity on Bonaire. WEB are working for over 20,000 households, companies and organizations on the island.\n\n\n\n\n\n\n\n\nAlliander\n\nAlliander develops and operates energy networks. Through their cables and pipes, over three million Dutch households and companies are supplied with electricity, gas and heating. They operate a 90,000km electricity grid and a 40,000km gas network, and take great pride in their networks being among the world’s most reliable. Our 7,000 employees make sure the lights are on, homes are heated and businesses can keep operating.\n\n\n\n\n\n\n\n\nIT Partners\n\n\nKPN\n\nAs the network of the Netherlands they are passionate about offering secure, reliable and future-proof networks and services, enabling people to be connected anytime, anywhere, whilst at the same time creating a more prosperous and cleaner world. They have been doing this on the basis of a strong vision. Every day, for almost 150 years.\n\n\n\n\n\n\n\n\nGovernment Partners\n\n\nCommission water management\n\nAn initiative by the Govt. of Curacao to ensure water availability, sustainability, safety and security\n\n\n\n\n\n\n\n\nCommission water management\n\nMinistry of Economic affairs, Govt. of Curacao"
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