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skills in scientific programming and quantitative analysis, preferably in python; affinity with climate science and/or policy modelling; excellent written and verbal communication skills in English
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access to an exceptional international research environment offering unique clinical facilities. Requirements: Master's in computer science or equivalent Python/C++ programming skills Strong knowledge
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
. Proficiency in programming and modern ML tooling (e.g., Python, PyTorch); experience with LLMs (e.g., using Hugging Face libraries) is a plus. Ability to reason about complex systems and turn ideas into code
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of the environments. A research-oriented attitude. Solid programming knowledge (Python, and preferably web development languages), and some experience with ABM or willing to learn ABM is a plus A good understanding of
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with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows
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collaborations with different international research networks. Additional qualifications Experience in programming languages (Python, C, Fortran, etc.) and with numerical codes for fluid dynamics is a merit
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. Candidates must have proven ability to work with large datasets, coding with Python/Fortran/C++ and ideally experience with high-performance computing. Applicants from an industry background are encouraged
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, empwering personalized medicine in psychiatry. Experience in quantitative genetics and/or deep neural networks, R/Python/PyTorch/Tensorflow etc. is desirable. Apply now via our career portal until 19.11.2025
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: At least an upper second-class degree (preferably MSc) in a Science or Technology discipline. Good working knowledge of machine learning and deep learning. Hands-on knowledge of Python or PyTorch
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. The following skills are highly desirable but not essential: Ability to program in Matlab/Python Experience with Finite Element Analysis and Reduce Order Modelling Experience in Rapid Prototyping and CAD Design