Kinaxis
Kinaxis
Kinaxis ऐसा सप्लाई चेन ऑर्केस्ट्रेशन सॉफ़्टवेयर विकसित करता है, जो संगठनों को जटिल परिचालनों की योजना बनाने, उन्हें प्रबंधित करने और उनके प्रति प्रतिक्रिया देने में मदद करता है। इसका Maestro प्लेटफ़ॉर्म एआई-सक्षम योजना, उन्नत विश्लेषण और रीयल-टाइम डेटा दृश्यता को एक साथ लाता है, जिससे सप्लाई चेन में अधिक सूचित निर्णय लेने में सहायता मिलती है। कंपनी के समाधान विनिर्माण, स्वास्थ्य सेवा और परामर्श सहित विभिन्न क्षेत्रों के संगठनों के लिए उपयोगी हैं और दक्षता बढ़ाने, मांग का पूर्वानुमान लगाने तथा व्यवधान आने पर तेज़ी से अनुकूलन करने पर केंद्रित हैं।

AI and Machine Learning Research Intern – Canada (Hybrid)

Research time-series forecasting and generative AI methods for supply chain planning at Kinaxis. Develop practical machine-learning capabilities through experiments, prototypes, and collaboration with research and product teams.

विवरण

  • Conduct applied machine-learning research in time-series forecasting and generative AI for supply chain planning
  • Review research literature, identify promising approaches, and define testable questions and hypotheses
  • Design and execute reproducible experiments with real-world and benchmark datasets
  • Build and assess models against strong baselines using quantitative and qualitative measures
  • Interpret findings, limitations, robustness, and practical trade-offs
  • Present research outcomes to both technical and non-technical audiences
  • Create research prototypes and work with researchers, data scientists, engineers, and domain experts to evaluate product potential
  • Contribute to product concepts, technical reports, demonstrations, publications, and patent applications when appropriate
  • Work with a dedicated mentor throughout the internship

आवश्यकताएँ

  • Currently pursuing or recently completed a master's or PhD in computer science, machine learning, statistics, applied mathematics, operations research, or a related discipline
  • Eligible for an internship through current full-time enrollment or graduation within 12 months of the placement end date
  • Strong grounding in machine learning, statistics, and experimental design
  • Practical AI/ML research experience from graduate work, publications, a thesis, research internships, or substantial projects
  • Proficiency in Python and experience with contemporary machine-learning frameworks
  • Able to turn open-ended problems into testable hypotheses, rigorous experiments, and well-supported conclusions
  • Experience with time-series forecasting, probabilistic forecasting, or time-series foundation models such as PFNs is an advantage
  • Familiarity with generative AI, including large language models, retrieval-augmented generation, and agents, is an advantage
  • Knowledge of statistical inference, uncertainty quantification, or causal inference is an advantage
  • Experience evaluating foundation models through fine-tuning, adaptation, benchmarking, or error analysis is an advantage
  • Research communication experience demonstrated through publications, preprints, or technical reports is an advantage
  • Must be based in Canada's Eastern time zone; interns in Ottawa or Toronto must work from the office at least three days per week

लाभ

  • Flexible vacation policy and company-wide Kinaxis Days off
  • Flexible working arrangements
  • Programs supporting physical and mental well-being
  • Regular virtual fitness classes
  • Mentorship, training, and career-development opportunities
  • Recognition programs and referral rewards
  • Hackathons

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