BMW’s EV Pioneer Stefan Juraschek Engages Kerala’s Emerging Engineers at Acsia Campus Connect
Sep 14, 2024

On the sidelines of the Acsia Global HQ and R&D Centre inauguration, students from top engineering colleges connected with Acsia and industry experts to explore the future of automotive technology.

Thiruvananthapuram, 27th August 2024 – In a remarkable gathering at the College of Engineering Thiruvananthapuram (CET), Stefan Juraschek, the visionary behind BMW’s electric vehicle revolution and now Strategic Advisor at Acsia Technologies, inspired over 160 engineering students with his deep insights into the future of automotive technology. Juraschek, revered as the “Father of EV Vehicles at BMW,” captivated the audience with his insights into the evolving world of automotive technology, particularly in the realm of electric vehicles (EVs). His message was clear: innovation, perseverance, and collaboration are key to shaping the future of the automotive industry.

Hosted by Acsia, a global leader in automotive software solutions specializing in Digital Cockpits & Displays, e-Mobility, and Telematics, the Acsia Industry Campus Connect Program provided an invaluable opportunity for students from 10 leading engineering colleges to connect with industry leaders, exchange ideas, and explore cutting-edge developments in e-mobility, digital cockpits, and more.

Acsia Campus Connect event was held on the sidelines of the inauguration of Acsia’s new Global Headquarters and Research & Development Centre at Embassy Taurus TechZone in Technopark Phase III campus in Thiruvananthapuram on August 22, 2024. Kerala Industries Minister P. Rajeeve inaugurated the event, which also featured speeches by Former Minister and Kazhakkuttam MLA Kadakampally Surendran, Principal Secretary APM Mohammed Hanish IAS, Christina Hein and German Ferreira of the BMW Group, Stefan Juraschek, Jijimon Chandran, Technopark CEO Col. Sanjeev Nair, Kerala Startup Mission CEO Anoop P. Ambika, and Taurus Investment Holdings India Head Ajay Prasad.

“At Acsia, we believe in the power of collaboration between academia and industry to drive innovation. The Acsia Industry Campus Connect event is a testament to this belief, providing a platform for students to engage with industry experts and gain insights that will help them shape the future of automotive technology. It’s incredibly rewarding to see the enthusiasm and potential of the next generation of engineers right here in Thiruvananthapuram,” said Jijimon Chandran, CEO & Founder of Acsia.

A central highlight of the event was a panel discussion moderated by Sojan James, Principal Architect and Subject Matter Expert at Acsia. The panel featured esteemed leaders from both academia and industry, including Jijimon Chandran, Stefan Juraschek, Nibil P. M., AVP – Technology at Acsia, and Dr. Savier, Principal of Government Engineering College, Idukki, and Former Principal of CET.

“Students had the unique opportunity to engage with domain experts in automotive software solutions. These programs offer mutual benefits for both academia and industry, fostering greater awareness among students about industry requirements. We need more initiatives like this to bridge the gap and prepare the next generation of engineers,” said Prof. Sasi N., Associate Professor, Department of Mechanical Engineering, CET, and Faculty Advisor, Society of Automotive Engineers (SAE India).

The discussion focused on critical topics such as e-mobility and vehicle electrification, digital cockpits and user experience, telematics and connected vehicles, and research and development in automotive technology. The panelists shared valuable insights into the challenges and opportunities within these domains, emphasizing the need for strong collaboration between educational institutions and the tech industry to prepare students for future technological challenges.

“The program was incredibly insightful, especially in understanding the latest in electric vehicle technologies. Interacting with Stefan Juraschek and the experts from Acsia gave us a real-world perspective that we don’t often get in the classroom. It was also a great opportunity to learn more about the work being done at Acsia,” said Priyadarsh K. V., a student from the Mechanical Engineering Department at CET.

“The Industry Connect sessions gave us valuable insights into real-world challenges in the automotive domain. The discussions and hands-on experiences not only deepened our understanding but also motivated us to strive for excellence in our future careers,” said Sharlet George Kurien, a third-year Computer Science & Engineering student at Saintgits College of Engineering, Kottayam.

An interactive Q&A session provided students with the opportunity to engage directly with the panelists. Participants posed thoughtful questions and received insightful responses that deepened their understanding of industry dynamics and expectations. This segment was particularly well-received, as it allowed students to clarify doubts and seek guidance on their future career paths.

The program not only inspired the next generation of engineers but also reinforced the importance of bridging the gap between academia and industry to drive technological advancements.

Kurien Noel Keeyath, Engineer at Acsia, and Greeshma K. R., Senior Engineer at Acsia, along with Dr. Suresh, Principal of CET, and Dr. Arun Surendran, Principal of Trinity College and Strategic Advisor to Acsia, also spoke at the event.

About Stefan Juraschek

With a notable career spanning more than three and a half decades at BMW Group, Juraschek is an expert in Electric Mobility, Electric Powertrain, Cybersecurity, and Vehicle Dynamics. During his tenure at BMW Group, Juraschek held multiple high-impact roles, including as Vice President of R&D for the Electric/Electronic department and General Manager of R&D for Electric/Electronic, Electric Powertrain, Inverters, and Power Management.

His contributions were instrumental in shaping BMW’s advanced technology and sustainability efforts, leading to ground-breaking advancements in Battery Electric Vehicles, Plug-In Hybrids, and Formula E Powertrains.

Press Contact

Athul Lal A G
Director of PR
Email: athul.lal@acsiatech.com
Mob: +91 81290 07793

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

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  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

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Build an AI-powered log analytics assistant that can:

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  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
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Goal

Deliver a working prototype that:

  • Operates on sample log data.
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  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
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Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

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  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
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  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
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AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
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Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.
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