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of Oxford and Valencia, on a large-scale European project. The focus of the postdoc project will be on using advanced numerical simulation modeling software and machine learning techniques to quantitatively
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current drift-diffusion modeling software, applying it to experimental data, and using machine learning to identify trends in the data. Applicants should have a PhD in physics, materials science, electrical
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machine learning, signal processing, epidemiology and causal inference, but all candidates with knowledge at the intersection of these three scientific disciplines are invited to apply. Candidates with
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European credits) in information economics/ operations management, econometrics, machine learning, extensive data analytics and qualitative research methods. The training can be adapted to the previous
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computer vision methods. Design and set up experiments. Deploy your algorithms on machines. Write well-documented code. Prepare demonstrators. Write scientific papers. Guide Master's and/or PhD students
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with your chip(s) will be analyzed with machine-learning algorithms. You will collaborate with researchers and companies of various disciplines like chemistry, embedded systems, software, signal
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research experience in a non-Dutch academic environment. You have a PhD in Computer Science, Artificial Intelligence, or Physics and have experience with machine learning. You have strong programming skills
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computer vision methods. Design and set up experiments. Deploy your algorithms on machines. Write well-documented code. Prepare demonstrators. Write scientific papers. Guide Master's and/or PhD students
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presentations at conferences) Demonstrable proactive and flexible attitude Has experience with molecular data and applying landscape modelling frameworks (e.g. SDMs) Particular strengths with Machine Learning
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implications of such systems? We are seeking a postdoc to join our team, someone who is keen to contribute to the development of trustworthy machine learning systems and to translating algorithmic advances and