Assistant Professor Artificial Intelligence for New Materials (Academic Career Track)

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Assistant Professor Artificial Intelligence for New Materials (Academic Career Track)

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Offer DescriptionShort description of relevance:We are seeking an Assistant Professor to join Delft University of Technology, with a strong focus on integrating Artificial Intelligence (AI) into material science research and education.This position offers the unique opportunity to lead groundbreaking research and shape the future of materials technology through AI-driven methodologies. The successful candidate will have research excellence in computer-based materials modelling applied to solve materials problems at the cutting edge, and a track record of AI applications in materials science, enabling one to bridge computations to experiments. Furthermore a drive to collaborate, and a passion for teaching at the university level are essential.The position will be embedded in the Department of Radiation Science and Technology. We are committed to advancing material science by fostering an interdisciplinary approach that merges traditional modeling techniques with modern AI technologies, and advanced experimental characterization. We provide a collaborative environment equipped with state-of-the-art facilities and support for ambitious projects that push the boundaries of what’s possible in material innovation.Join us in our mission to develop smart materials that address real-world challenges and to educate the next generation of scientists who will continue transforming the landscape of material science.Focus of the position:The focus of this position is on pioneering the integration of advanced AI methodologies into material science research and education. The successful candidate will be expected to drive innovative research projects that leverage cutting-edge AI technologies such as deep learning, neural networks, and machine learning algorithms, combined with advanced modeling techniques to develop new materials and enhance material properties in ways that were previously unattainable.Key area of focus should be on AI-driven Material Discovery and Design. This includes utilizing AI to predict and generate new (functional) materials with desired properties for use in industries. This involves the use of sophisticated AI tools to simulate and model material behaviors at atomic and molecular levels, i.e. combined/interfacing with DFT methods, far beyond traditional computational methods. The successful candidate’s research is expected to bridge time and length scales, from atomistic to macroscopic, to describe the corresponding materials properties and phenomena. Preferably your research should align with our experimental material science research groups, focusing on energy materials, and using extensively our neutron- and positron-based characterization methods.Furthermore, your research will be embedded in the Delft AI Initiative. Bringing fundamental and applied AI together makes it possible to push the boundaries of science. ‘AI, Data & Digitalisation’ at TU Delft connects AI experts and scientists applying AI in their field, driving research and innovation. In this position you will be provided the opportunity to increase the capacity of inter-disciplinary research and education in digital transformation. The position is part of the TU Delft broad AI Labs & Talent programme and offers tenure-track positions to outstanding academics active in AI or digitalisation-related research areas to find answers to major scientific and societal challenges.Education:As an assistant professor, you will also contribute towards our educational program at the bachelor and master level. This may include developing and teaching courses on topics such as material sciences, programming, numerical methods, computational material science, or AI.Where to apply WebsiteRequirementsSpecific RequirementsThe ideal candidate for this position should possess a Ph.D. in Material Science, Physics, Chemistry, Computer Science, or a closely related field, with a demonstrated expertise in artificial intelligence, machine learning, and computational atomistic and multiscale modeling as applied to material science. We expect this position asks for generative AI as physics-informed generative models ensure that generated samples respect physical laws (e.g., thermodynamic stability), which is crucial in our (energy) material science applications. This expertise should extend beyond traditional nonlinear regression techniques, including substantial experience with advanced AI methods such as active learning, geometric deep learning, generative AI, reinforcement learning, and other cutting-edge computational approaches applied to material science.

Candidates should have a strong track record, showcasing significant contributions to the field of material science and technology. Additionally, proven experience in securing research funding and a commitment to innovative teaching at the university level are essential. The ability to collaborate effectively within interdisciplinary teams and contribute to ongoing projects or lead new initiatives is also expected. Familiarity with the latest software and analytical tools in both AI and material science is crucial for success in this role.Additional InformationBenefitsThis position is offered as an Academic Career Track position (0.8 – 1.0 FTE). During the Academic Career Track, we expect you to grow towards an Associate Professor position within a maximum of eight years, for which a position will be available. With other Academic Career Track colleagues, you will participate in the Academic Career Track Development programme, where you are offered ample opportunities to develop yourself in the areas of Education, Research, Societal Impact & Innovation, and Leadership & Organisation. You will regularly discuss your development and results with senior staff based on a personalized development plan and performance criteria agreed upon at the start of your Academic Career Track. You will start with a temporary contract that will be converted to a permanent contract no later than 12 -18 months after a positive evaluation, based on continuous confidence in your development potential and fit in the organisation.Inspiring, excellent education is our central aim. We expect you to obtain a University Teaching Qualification (UTQ) within three years if you have less than five years of teaching experience. This is provided by the TU Delft UTQ programme as part of the Academic Career Track Development programme.TU Delft sets high standards for the English competency of the teaching staff. The TU Delft offers training to improve English competency. If you do not speak Dutch, we offer courses to learn the Dutch language within three years.Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged and you can work partly from home.For international applicants, TU Delft has the . This service addresses the needs of new international employees and those of their partners and families. The Coming to Delft Service offers personalised assistance during the preparation of the relocation, finding housing and schools for children (if applicable). In addition, a for partners is offered. The Coming to Delft Service will do their best to help you settle in the Netherlands.Selection processAre you interested in this vacancy? Please apply no later than 1 November 2024 via the application button and upload your motivation letter, CV and your research and teaching vision.

• A pre-employment screening can be part of the selection procedure.
• Applying for an exemption for specific research and educational areas is an obligatory part of the selection procedure for this vacancy. This exemption must be obtained from the Ministry of Education, Culture and Science (OCW) before an employment contract is agreed upon. Click

for more information. * You can apply online. We will not process applications sent by email and/or post.

• Please do not contact us for unsolicited services.

Additional commentsIf you have any questions regarding the position feel free to contact Prof.dr.ir. Jan-Leen Kloosterman, chair Department Radiation Science and Technology, via Website for additional job detailsWork Location(s)Number of offers available 1 Company/Institute Delft University of Technology Country Netherlands City Delft Postal Code 2628 CD Street Mekelweg 2 GeofieldContact CityDelft WebsiteStreetMekelweg 2 Postal Code2628 CDSTATUS: EXPIREDShare this page

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Location

Delft, Zuid-Holland

Job date

Sun, 29 Sep 2024 01:11:02 GMT

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