Associate Director, Computer Vision and Ai
vor 6 Monaten
When we put unexpected teams in the same room, we ignite bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn't mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.
**ABOUT ASTRAZENECA**
At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.
**SITE DESCRIPTION**
Welcome to our Computational Pathology site in Munich. One of over 400 sites here at AstraZeneca, providing a collaborative environment where everyone feels comfortable and able to be themselves is at the core of AstraZeneca’s priorities, it’s important to us that you bring your full self to work every day. To help you maintain your best self, here’s a sneak peek into some of the things this site provides for you: After-work events, Lunch & Learns, bright and spacious environment, sustainable office working environment, networking events, family and childcare support and of course the Alps around the corner for hiking, biking and skiing.
**BUSINESS AREA**
Join us on our journey to becoming digitally-enabled, to discover new ways of offering better solutions to our patients. We are a science-based, leading and decisive Oncology enterprise united in our vision to eliminate cancer as a cause of death. We foster a mindset of courage, where everyone is empowered to step up, innovate and work at pace. Our commitment to patients and pioneering spirit are truly embedded and it's what makes our pipeline unique."
**What you’ll do**
- Serving the goals of drug development projects, create image analysis solutions to generate decisive data based on the analysis of H&E, immunohistochemistry (IHC), and multiplexed immunofluorescence (mIF) whole slide images
- Assume an image analysis leadership role in projects or segments of large-scale drug development programs
- Disseminate knowledge, best practices and developed algorithms and software tools to internal audiences with project meetings, seminars and documentation as well as to the external academic community with conference presentations and journal publications
- Collaborate closely with project stakeholders and interfacing functions such as translational science, pathology, quality assurance, data management, software development, data science and bioinformatics.
- Comply with the AstraZeneca General Laboratory Standard to support product quality and regulatory compliance in the development, manufacture and distribution of Active Pharmaceutical Ingredients, Medicinal Products and Devices.
**Essential for the role**
- Ph. D. degree in computer sciences, mathematics, physics, bioinformatics or comparable degree with a focus on deep / machine learning and computer vision; or equivalent experience e.g. 3 years in industry.
- Additional working experience in the field of medical and/or biomedical image analysis as post-doc or in industry
- Expert knowledge of state-of-the-art deep learning models and architectures for computer vision in particular for object detection, semantic segmentation, instance segmentation and weakly-supervised learning
- Advanced programming skills in Python and good knowledge of the most common scientific computing libraries (e.g. numpy, pandas, scikit-learn, scipy, opencv) and machine learning frameworks (e.g. tensorflow, pytorch).
- Good knowledge of best practices for software development (e.g. docstring, unit testing, CI pipelines) as well as for the development, validation and release of models (e.g. data splitting, model testing, hyperparameter tuning)
- Proven track record of scientific publications, conference contributions and/or delivered AI solutions in scientific industry projects
- Accountability for sub-project management and delivery in a multi-national, multi-disciplinary project in academia or industry
- Talent to conceptualize, build, communicate and implement novel scientific ideas aligned with the drug development and biomarker strategies
**Desirable for the role**
- Working experience with at least one of the following: semi-supervised learning, self-supervised learning, few-shot learning, generative learning, self-attention learning (e.g. Transformers), active learning, survival learning, graph neural networks and / or domain adaptation learning.
- Knowledge on clinical biostatistics e.g. Kaplan Meier analysis, feature selection, cross-validation and multiple testing. Working experience with the analysis of translational science data in the biopharmaceutica
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