Researcher in image analysis/machine learning for zebrafish data
The deadline for applying to this position has passed.
Uppsala University is a comprehensive research-intensive university with a strong international standing. Our mission is to pursue top-quality research and education and to interact constructively with society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has 46.000 students, 7.300 employees and a turnover of SEK 7.3 billion.
The Department of Immunology, Genetics and Pathology at Uppsala University (www.igp.uu.se) has a broad research profile with strong research groups focused on cancer, autoimmune and genetic diseases. A fundamental idea at the department is to stimulate translational research and thereby closer interactions between medical research and health care. Research is presently conducted in the following areas: medical and clinical genetics, clinical immunology, pathology, neuro-oncology, vascular biology, radiation science and molecular tools. Department activities are also integrated with the units for Oncology, Clinical Genetics, Clinical Immunology, Clinical Pathology, and Hospital Physics at Akademiska sjukhuset, Uppsala. The department has teaching assignments in several education programmes, including Master Programmes, at the Faculty of Medicine, and in a number of educations at the Disciplinary Domain of Science and Technology. The department has a yearly turnover of around SEK 420 million, out of which more than half is made up of external funding. The staff amounts to approximately 345 employees, out of which 100 are PhD-students, and there are in total more than 700 affiliated people.
One position as a researcher is available in Dr Marcel den Hoed’s group. The research group aims to translate findings from large-scale -omics studies for cardiometabolic diseases in humans using in vivo, largely image-based zebrafish model systems.
Project description: Genome-wide association studies (GWAS) have identified hundreds of genetic loci that are robustly associated with cardiometabolic risk factors and diseases. With few exceptions, the causal genes in these loci remain uncharacterised. The overall aim of our research is to identify and characterise causal genes in GWAS-identified loci (amongst others) for cardiometabolic risk factors and diseases using zebrafish model systems. Candidate genes are targeted using CRISPR/Cas9, and a range of validated, disease-related traits are captured in each and every larva using a fluorescence microscope and automated positioning system.
Duties: The researcher will generate and validate algorithms that can quantify imaging data in a hypothesis-driven and hypothesis-generating manner. Since these algorithms will be used by most other researchers in the group, the successful candidate will be a spider in the web.
Key tasks include: 1) developing algorithms to quantify image-based traits related to liver fibrosis and kidney oedema; 2) adapting and further improving existing algorithms for atherosclerosis- and diabetes-related traits; 3) developing algorithms to compare larvae with and without mutations in candidate genes holistically; 4) liaising with other researchers in the team to optimise algorithms in an iterative manner.
The successful candidate will be placed in the den Hoed research group (http://igp.uu.se/forskning/ genetik_genomik/marcel-den-hoed/) and will be jointly supervised by Drs den Hoed and Allalou. The project is funded by a collaboration with industry.
Requirements: Applications are accepted from highly motivated candidates with a PhD in scientific computing, computer science, applied mathematics, computer vision, bioinformatics, image processing, or similar, and at least 2 years of post-doctoral experience working with segmentation and quantification of high-throughput zebrafish data using image analysis and machine learning/deep-learning approaches. Experience working with the Vertebrate Automated Screening Technology (VAST) BioImager in combination with automated fluorescence microscopy is also required. The candidate should have experience working with image analysis and deep-learning using MATLAB and python. Hands on experience with microscopy imaging of zebrafish is a merit, as is experience with optical projection tomography.
A successful candidate should be a goal-oriented, highly motivated, organised, reliable team player that can also work independently. Being able to plan ahead and prioritise in accordance with project goals and deadlines are essential characteristics of a successful candidate. Experience with handling large datasets is also required, as is written and oral proficiency in English.
The application should include a cover letter describing yourself, your research interests, your experience with the requirements described above, and your availability. A CV; a list of accepted or published papers in peer-reviewed journals; a PhD certificate; and contact details for at least two reference persons should also be included. Letters of recommendation can be included.
Salary: Individual salary.
Starting date: as soon as possible.
Type of employment: Temporary position of 18 months, with the possibility of extension.
Scope of employment: 100 %
For further information about the position please contact:
Marcel den Hoed, firstname.lastname@example.org, 070-4250752
Please submit your application by September 24 2020, UFV-PA 2020/3093.
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Submit your application through Uppsala University's recruitment system.
Placement: Department of Immunology, Genetics and Pathology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Individual salary
Number of positions: 1
Working hours: 100%
County: Uppsala län
Number of reference: UFV-PA 2020/3093
Last application date: 2020-09-24
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