Destinus je evropsko letalsko in obrambno podjetje, ki razvija avtonomne letalne sisteme za civilne in obrambne namene. Podjetje, ustanovljeno leta 2021, načrtuje in izdeluje letalske tehnologije za različne razpone hitrosti, vključno s prihodnjimi hipersoničnimi platformami. Njegovo multidisciplinarno delo združuje načrtovanje letalskih konstrukcij, turboreaktivni pogon, programsko opremo za upravljanje leta, umetno inteligenco in celovito načrtovanje sistemov. Z rastjo ekipe Destinus ponuja priložnosti za sodelovanje pri razvoju avtonomnih, učinkovitih in visokohitrostnih letalskih sistemov.
Machine Learning Researcher, Aviation Safety Certification
Research mathematical safety guarantees for autonomous-flight AI at Destinus and Daedalean, connecting statistical learning theory with EASA aviation certification frameworks.
Opis
- Review and develop certification strategies for supervised machine learning.
- Align mathematical guarantees, technical arguments, and model evaluations with the EASA W-shaped AI certification process.
- Investigate statistical learning theory, generalization bounds, and guarantees for deep neural networks in changing real-world conditions.
- Use statistics and stochastic processes to model environmental uncertainty and sensor noise, and assess confidence in performance metrics.
- Assess research literature, mathematical proofs, and engineering assumptions with peer-review rigor.
- Develop technical justifications and logical arguments for safety audits and certification frameworks.
- Write reproducible Python code to test hypotheses, validate certification methods, and benchmark model safety frameworks.
Zahteve
- Bring a deep understanding of statistical learning theory, generalization bounds, and the mathematical foundations of deep neural networks.
- Have a strong foundation in advanced probability, statistical modeling, and stochastic processes.
- Be proficient in Python and able to write clear, reproducible code; basic coding skills can be learned on the job with strong theoretical foundations.
- Be able to conduct thorough literature reviews, identify gaps in current AI research, and translate academic concepts into engineering frameworks; this can be learned with a strong quantitative foundation.
- Have experience or ability in peer review, including critical assessment of mathematical proofs, algorithmic assumptions, and research papers.
- Be pursuing or have recently completed a Master's or PhD in Computer Science, Mathematics, Theoretical Statistics, Electrical Engineering, Physics, or a related quantitative research field.
- Use critical thinking and maintain healthy skepticism toward machine learning assumptions and empirical accuracy.
- Be exceptionally capable of building sound logical arguments and technical justifications.
- Be motivated to tackle mathematical problems spanning theoretical computer science, safety engineering, and real-world flight operations.