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Field
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data analysis, modelling, and computational workflows, where appropriate. Qualifications: Strong programming skills in R and/or Python. Ability to develop reproducible computational workflows and
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datasets (bulk, single-cell, and spatial). Primary responsibilities include developing sequencing pipelines, maintaining project databases, designing validation experiments, and drafting manuscripts and
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laboratory analysis is an advantage. Additional Information Benefits What do we offer. A fully-funded PhD position, with an initial appointment for 1 year, which will be prolonged with 3 years after a positive
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spatial and temporal variability in sediment accumulation and vegetation development. This PhD project is part of a larger interdisciplinary research initiative aiming to enable a transition towards more
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/or adapt machine learning and artificial intelligence approaches for the analysis of spatial and multispectral imaging datasets, including transcriptomic, proteomic, and lipidomic data derived from
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for the analysis of spatial and multispectral imaging datasets, including transcriptomic, proteomic, and lipidomic data derived from tissue specimens and cell culture models. • Prepare figures, reports, manuscripts
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and damaging storms, and cascading risks such as bark-beetle outbreaks. Forest owners and regional authorities need spatially detailed, timely information on where climate stress is emerging, and which
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modalities, spatial scales, and diverse populations. Position Highlights Work with cutting-edge multimodal concurrent stimulation-recording datasets spanning iES, TMS, iEEG, single-unit, and fMRI. Study causal
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Biological sciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Oct 2026 - 23:59 (Europe/Brussels) Country Spain Type of Contract Temporary Job Status Full
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and a completed PhD in computer science, data science, computational science or a comparable quantitative discipline. Applications from candidates who are close to completing their PhD are welcome