Destinus ir Eiropas aviācijas un aizsardzības uzņēmums, kas izstrādā autonomas lidojumu sistēmas civilām un aizsardzības vajadzībām. Uzņēmums, kas dibināts 2021. gadā, projektē un ražo lidojumu tehnoloģijas dažādiem ātruma diapazoniem, tostarp nākotnes hiperskaņas platformām. Daudznozaru darbā apvienota lidaparātu konstrukciju inženierija, turboreaktīvie dzinēji, lidojumu programmatūra, mākslīgais intelekts un visaptveroša sistēmu projektēšana. Destinus paplašinot savu komandu, uzņēmums piedāvā iespējas piedalīties autonomu, efektīvu un lielā ātrumā lidojošu gaisa kuģu sistēmu izstrādē.
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.
Apraksts
- 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.
Prasības
- 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.