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Field
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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the BSc and MSc programs of the ENR Group; and actively contributing to a dynamic, inclusive, and collaborative research culture within the research group. You will work here The research is embedded within
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comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and mapping them onto phylogenetic trees Collaborating with a multidisciplinary team of biomechanists
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requirements The candidate should have an MSc degree (or equivalent) in one of the following fields: • (Marine) Biology • (Marine) Ecology • Marine Sciences • or a related discipline Profound knowledge and hands
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organizing collective behaviour Analysing interspecific variation in swarming behaviour using comparative and statistical approaches Exploring evolutionary hypotheses by clustering behavioural traits and
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that encompasses research units in Chemical Ecology, Resistance Biology and Integrated Plant Protection. Both applied and fundamental research are performed at the department, providing an excellent learning
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techniques to map gene regulatory networks (e.g. ChIP-seq, RNA-seq) and statistical approaches to discover genotype-phenotype associations (e.g. GWAS, random forest) in common-garden and aquaculture
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, statistical mechanics and/or probabilistic machine learning. You should be willing to take an ambitious and unconventional approach to hard problems and be motivated by a desire for both deep understanding and
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with a wide range of data formats and engaging with data experts and database managers. The second major focus is advanced data analysis and statistical modeling to identify patterns in fish distribution
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. This comprises learning to set up and operate our new optical cryostat platform, which involves advanced confocal microscopy, laser pulse shaping, and time-bin interferometry. (b) You will benefit from our