The Complex Trait Genetics lab (www.ctglab.nl) at the Center for Neurogenomics and Cognitive Research in Amsterdam (www.cncr.nl) is seeking applicants for a PhD position in Genetics and Addiction. The successful candidate will be part of the BRAINSCAPES project, which is a 10-year nationwide collaboration funded by the Dutch Government (Gravitation Program), and led by the department of Complex Trait Genetics. The main aim of BRAINSCAPES is to gain mechanistic insight into several brain disorders by leveraging results from recent genetic discoveries, utilizing large-scale bioinformatic resources, and testing genetically informed hypotheses in experimental studies.

The work of the candidate will focus on applying existing tools (e.g. FUMA, MAGMA, TWAS, LDSC, fine mapping) that integrate bioinformatic resources with results from genetic discovery studies for brain-related disorders, with a focus on addiction. The aim of the project will be to identify the neurobiological mechanisms linking genes to addiction susceptibility and investigating their overlap with other neuropsychiatric disorders. The candidate will work in an enthusiastic team of PhD students and postdocs with diverse backgrounds including statistics, bioinformatics, artificial intelligence, neuroscience, psychology, and stem cell biology.

We are seeking a highly motivated, talented individual. The successful candidate needs to have experience with genetic research, an affinity for working with large datasets, and familiarity with genome-wide association studies. Knowledge of the UNIX environment, R, some scientific programming experience, and genetics and/or neuroscience background is preferred.

Your duties

• apply bioinformatic tools for integrating results from genome-wide association studies with biological resources to data on brain-related traits
• actively take part in the BRAINSCAPES consortium including frequent contact with other, mostly neuroscience oriented, labs
• authoring and co-authoring manuscripts submitted to high quality journals
• some support (10% of time) for teaching is expected
• frequently present work to the international scientific community

• MSc in any of the of the following fields: statistical or behavioral genetics, bioinformatics, computational biology or related computational field
• strong ability to write and read in English
• experience with Python, the R statistical language, and working in a UNIX environment is preferred
• demonstrable experience in (large) genetic data analysis is preferred
• support ‘open science’: share tools and data with the wider scientific community

A challenging position in a socially involved organization. The salary will be in accordance with university regulations for academic personnel and amounts €2,541 (PhD) per month during the first year and increases to €3,247 (PhD) per month during the fourth year, based on a full-time employment. The job profile: is based on the university job ranking system and is vacant for at least 0.8 FTE.

The appointment will initially be for 1 year. After a satisfactory evaluation of the initial appointment, the contract will be extended for a duration of 4 years. Additionally, Vrije Universiteit Amsterdam offers excellent fringe benefits and various schemes and regulations to promote a good work/life balance, such as:

• a maximum of 41 days of annual leave based on full-time employment
• 8% holiday allowance and 8.3% end-of-year bonus
• solid pension scheme (ABP)
• optional model for designing a personalized benefits package

Vrije Universiteit Amsterdam

The ambition of Vrije Universiteit Amsterdam is clear: to contribute to a better world through outstanding education and ground-breaking research. We strive to be a university where personal development and commitment to society play a leading role. A university where people from different disciplines and backgrounds collaborate to achieve innovations and to generate new knowledge. Our teaching and research encompass the entire spectrum of academic endeavor – from the humanities, the social sciences and the natural sciences through to the life sciences and the medical sciences.

Vrije Universiteit Amsterdam is home to more than 30,000 students. We employ over 5,500 individuals. The VU campus is easily accessible and located in the heart of Amsterdam’s Zuidas district, a truly inspiring environment for teaching and research.

Diversity
We are an inclusive university community. Diversity is one of our most important values. We believe that engaging in international activities and welcoming students and staff from a wide variety of backgrounds enhances the quality of our education and research. We are always looking for people who can enrich our world with their own unique perspectives and experiences.

The Faculty of Science
Working at the Faculty of Science means working together with students, PhD candidates and scientists who are focused on their discipline, yet also have a broad view of the world. We are proud of the collegial working environment within the faculty, which is characterised by an ambitious and pragmatic attitude with commitment to the bigger picture. At the Faculty of Science, scientists and students work on fundamental and complex social issues for a sustainable and healthy future. From forest fires to big data, from obesity to malnutrition, from genetics to pharmaceuticals and from molecules to the moon: our teaching and research cover the full breadth of the natural sciences. Scientific teaching and research is highly experimental, technical, computational and interdisciplinary in nature. This is why we collaborate extensively with leading research institutes and industry. The faculty has over 11,000 students studying across 40 educational programmes and employs over 1,600 staff spread over 10 scientific departments, making us the largest science faculty in the Netherlands.

About the department Complex Trait Genetics
Research at the department of Complex Trait Genetics focuses on identifying the genetic mechanisms of brain-related traits. We develop novel statistical and bioinformatics tools to optimally analyze large genome-wide data sets for complex traits, combine them with biological data and generate hypotheses that can be tested in functional studies to obtain mechanistic insight.