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PhD in Vascular Biology, Neuroscience, Molecular Biology, Computational Biology, or a related field Expertise in single-cell and spatial omics analysis (scRNA-seq, ATAC-seq, CUT&RUN, MERFISH, Visium
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to characterize seasonal krill flux in the region Your Profile PhD in biology, marine biology, fishery management and conservation, or related fields. Strong quantitative skills (statistics, modelling) and
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the excellence cluster Your profile PhD in physics or in a related discipline experience in phase contrast or coherent X-ray imaging advanced programming skills in a high-level computer language (Python or related
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
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coupled hydrological and groundwater modelling, combined with machine learning techniques, will quantify groundwater recharge and groundwater resilience. Your responsibilities: Analyse the dynamics
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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learning approaches for high-dimensional data, and programming skills in R or Python. Profile Track 2 – Experimental Immunology / Bone Marrow Biology / Trained Immunity Candidates with an experimental
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The Leibniz Institute for Neurobiology (LIN) is an internationally recognized neuroscientific research institute and dedicated to the research on learning and memory. Our research comprises all
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
for the sustainable management of selected marine ecosystems. Your Profile A PhD in marine biology, conservation biology, fishery management & conservation, or related fields A strong background in handling large data
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM