We are looking for people who want to accelerate the use of AI
Research in AI is moving at a rapid pace and has already demonstrated its ability to drive disruptive developments in both business and society. AI has the potential to reshape industries, transform the labor market, and fundamentally streamline traditional structures. Now we need to translate knowledge into more working solutions. Do you want to be part of making AI work for real?
About AI Lab
Chalmers Next Labs is establishing a new lab focusing on the use of AI - AI Lab for Science and Society. In our new AI lab, AI is tested, validated and scaled. The lab offers expertise and a practical test environment where authorities, companies and researchers get help to solve concrete challenges in, for example, healthcare, transport and production - and find solutions that can be used quickly in reality.
The profiles we are currently looking for
As a first step, we are recruiting about ten senior scientists and engineers in data management, modeling and validation to the lab.
The profiles we are currently looking for are:
Leader of the AI Lab
- Driven leader with a deep interest in technology and with experience from both industry and academia
Data collection and preparation
- Collection of data via APIs, databases, sensors or manual input, and identification of appropriate model architectures.
- Curation and preprocessing of data and datasets and models.
- Ensuring ethical management, data protection (GDPR) and legal compliance.
- Close collaboration with data scientists, ML engineers and subject matter experts to align data basis with model requirements.
Modeling and training
- Design, development and training of machine learning models.
- Performance optimization through iterative training, validation and model evaluation.
- Model integration in production environment in collaboration with software and platform engineersOngoing monitoring of model performance and maintenance and retraining when needed.
Validation and deployment
- Validation of the accuracy, generalizability and robustness of the model in different scenarios.
- Bias detection and management, data drift and conceptual drift.
- Collaboration with development teams to ensure high reliability and scalability in deployment.