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responsibilities Processing, analyzing and interpreting gene expression data across various biological contexts, including ribosome profiling, single-cell CRISPR methods such as CROP-seq, single-cell RNA sequencing
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project execution and adherence to quality standards PhD in life sciences or a related field Proven experience with in vitro models and assays Strong hands-on experience in cell culture and biochemical
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processes, particularly in complex or technically challenging settings. To address these questions, the lab develops and applies new methods for metabolomics, lipidomics, and ¹³C metabolic flux analysis, as
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: Demonstrating scalable architectures for fault-tolerant quantum information processing, featuring reconfigurable local control, rapid quantum non-demolition readout, and fast error-syndrome extraction Developing
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. Where to apply E-mail lorraine.dubuis@unige.ch Requirements Research FieldPsychological sciencesEducation LevelPhD or equivalent Skills/Qualifications Desired profile: PhD in psychology, neuroscience
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experimental findings will be part of your activities. You are expected to publish your research output in high-level peer-reviewed scientific journals. Your profile You must hold a PhD in Physics, Chemistry
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requirement of the position. Applicants must hold a PhD degree (or expect to obtain one before the start of the position). Application Materials Required: Further Info: Theoretical Physics Department, CERN CH
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modulators. Your profile We are looking forward to applications from highly motivated researchers (m/f/d) with a basic education in the life sciences and a PhD in biochemistry, molecular biology, or equivalent
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pipelines to support the labs long-term research interests Proactively manage the labs genomic data resources Supervise and mentor PhD and Masters students in comparative fungal genomics Assist in
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX