3 Machine Learning Jobs in Oberösterreich
Your future responsibilities
- Leading and developing the Embedded Artificial Intelligence research unit in the Intelligent Wireless Systems Division with currently 14 scientists focusing on machine learning, signal processing and neural network VLSI accelerators for a wide range of domains and applications.
- Vision for long-term research opportunities in electronic systems and miniaturization of machine learning and signal processing algorithms into hardware.
- Shaping the division research program and project portfolio together with supervisors and peers.
- Collaborate with the diverse team of experts to find optimal solutions.
- Budget and ressource planning and control for the research unit.
- Overall, lead the research unit to generate high quality research outputs, scientific excellence and economic impact. Keep growing the research unit.
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Your future responsibilities
- Lead a diverse team of 8–10 scientists within the Embedded Artificial Intelligence group of the Intelligent Wireless Systems Division, focusing on machine learning and signal processing across various domains and applications.
- Shape the research agenda and project portfolio in collaboration with team members, peers, and the Head of Research Unit
- Drive innovative solutions through effective team leadership and expert coordination. .
- Manage resource planning and allocation for the research team
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RESPONSIBILITIES
- 4+ years of experience in Data Science. Previous experience working as a Machine Learning Expert is a plus
- Masters or Doctorate in relevant field, for instance: Mathematics, Statistics, Science (including data, behavioural, social, econ, ...)
- Good command of English, both spoken and written. German a plus
- Programming: know at least one of R, Python, SQL with the ability and willingness to learn the other two
- Data Literacy: ability to prepare datasets ensuring top quality such that essence of said data and the implications for the problem at hand can be grasped quickly
- Statistical Reasoning: theoretical and applied understanding of Stats, Probability and ML Algorithms and know how to use these tools to reduce uncertainty in a business context (regression, visualization)
- Technical Literacy: applied understanding of modern computing allows the candidate to do things which go beyond the strict definition of Data Science (git, API calls, web crawling, …)
- Presentation Skills: present coherent data stories at the appropriate level of abstraction given the audience
- Stakeholder Management: skilled at building mutually beneficial connections with functional stakeholders
- Pragmatic Critical Thinking: intuitively consider relevant costs/benefits in all decisions and act accordingly
- Outcome Driven: highly motivated to add value and to demonstrate that impact to the organization
- Scientific Reasoning/ Scoping: ability to define and formulate new questions, in addition to answering given ones
- Grit: proven capability to see things through to the end even if initial feedback is discouraging
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