I-2422 Post-doctoral Researcher in Ai Benchmarking

Description
**24 months contract | Belval**:
**Are you passionate about research? So are we Come and join us**:
The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

**How will you contribute?**:
Within the **Trustworthy AI group**, we are looking for a post-doctoral researcher with **strong AI benchmarking expertise** for a European Defence Funds project. If you are passioned about AI, research with a strong impact, and have an interest for working alongside world-class defence actors, let us know of your profile.

**Definition of AI Metrics and Experimentation**
- Semantic segmentation metrics: class
- / task-specific and/or instance-level metrics, pixel-level, panoptic, metrics for resilience, and use-cases that target real-time and long-term performance evaluation, model complexity,
- Hardware-specific AI metrics: inference speed, memory usage, power consumption, single/double-precision, performance on GPUs, TPUs, edge,

**Impact of Data on Performance**
- Impact of data split and augmentation on model performance, overfitting, sensitivity to class imbalance, transfer-learning and fine-tuning performance
- Data augmentation for testing model resilience & re-assessment: occlusion, jittering, regularization techniques, geometric and/or photometric transformations, inpainting,
- GAN-driven augmentation / adversarial synthetic data generation for challenging a model’s performance, domain adaptation (e.g., image-to-image translation, style transfer augmentation)
- Temporal and spatial split: concept drift, train vs inference, class-aware split (low-representativeness class benchmarking, long-tail distribution), out-of-distribution generalization

**Semantic Segmentation Explainability Benchmarking**

**Hyper-model / model sampling, latent model space metric learning**

**Reproducibility and metrics**

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**Tooling; standardized benchmark protocol(s) and reporting**
- Experience with tooling for AI model versioning, experiment tracking, MLOps & orchestration, containerization, model serving, REST APIs
- Reporting and support for post-benchmarking drill-down on results

We offer a highly dynamic environment as part of a team that is active across different technology-transfer areas (AI benchmarking, explainability, embedded/edge AI,). LIST also offers a wide range of opportunities for training, as well as engaging with world-class researchers and labs.
- Have a strong ML/AI background, hold a PhD in computer science, mathematics or similar, and be able to showcase a track record in AI benchmarking research.
- Hands-on experience and a track record of projects carried using frameworks like Pytorch and/or Tensorflow is an absolute must.
- Any experience in working with e.g. Kubeflow, Kubernetes / associated tooling, and benchmarking frameworks like MLPerf / others, is a plus while not a strong requirement.

Operational knowledge and fluency in English is mandatory; French can be a plus while not required nor mandatory.

Any previous experience in a defense-oriented context is a plus while not mandatory; similarly, having been granted an EU Personal Security Clearance in the past can be a strong advantage.

**Your LIST benefits**:
An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects

Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society

Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do

An environment encouraging curiosity, innovation and entrepreneurship in all areas

Personalized learning programme to foster our staff’s soft and technical skills

Multicultural and international work environment with more than 50 nationalities represented in our workforce

Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions

Gender-friendly environment with multiple actions to attract, develop and retain women in science

32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance

Flexible working hours, home working policy and access to lunch vouchers

A motivation letter oriented towards the position and detailing your experience

A scientific CV with contact details

List of publications (and patents, if applicable)

Contact details of 2 references

**Application procedure and conditions**:
We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information