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The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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funded German research initiative. Project Description: Carbon black is an indispensable component of numerous everyday products – from car tires and seals to paints and plastics. However, its production
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
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For a behavioral experimental economics project on ‘Mental Models and Discrimination’ (leadby Kai Barron, PhD) in the Research Department ‘Market Behaviour’ within the Research Area ‘Markets and
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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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, conservation genomics, museomics, metagenomics, annotation, machine learning, Instruct users in the usage of hardware and software for molecular biodiversity research, Acquire substantial third-party funding
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looking for student assistants: Leibniz-Project LAB2 (lead by Dr. Levent Neyse) and DFG-Project ‘Mental Models and Discrimination’ (lead by Kai Barron, PhD). Please note: The list of tasks and duties below
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta