Clockwise from top left: Mr Isaac Hee, Managing Director, gradsingapore, Mr Albert Kok, Programme manager in AMD Global Operations, Business Operations, AMD, and Mr Chern Ann, EcoStructure lead, Plant & Machinery, Schneider Electric

Trying to find a spot that brings both job satisfaction and future opportunity in the sprawling engineering industry might be a little overwhelming, especially as a fresh graduate.

To address the matter, during gradsingapore’s virtual Enginuity 2020 event, we hosted a panel discussion, Opportunities in the Future of Engineering. There, several experts, namely Mr Albert Kok, Programme Manager in AMD Global Operations, Business Operations, AMD, and Mr Chern Ann, EcoStructure Lead, Plant & Machinery, Schneider Electric took the lead!

High-performance computing

Because today’s world is more data-driven than ever before, high-performance computing (HPC) has emerged as the go-to solution businesses need to gain insights into their industries, from climate analysis to astrophysics. Initially the fief of a select few large organisations, more and more enterprises are now turning to it to interpret their big data.

“There is now just so much data coming in that high-performance computers are needed to enable research and development,” Kok said.

In this growing field – estimated by Research and Markets to become a US$22 billion industry by 2026 – engineers use a combination of computing resources in order to tackle complex problems using multiphysics simulation.

As more and more businesses in an array of fields are now more dependent on data and its interpretations, engineers familiar with high-performance computing (HPC) are now in high demand, as they can harness the proper configurations to address these issues, increasing efficiency and providing the freedom to explore engineering and design permutations to arrive at possible solutions.

One of the most important skills needed in HPC is a strong problem-solver mentality. Other essential skills include an ability to quickly identify bottlenecks, as well as an understanding of science and Linux. If you have these skills along with strong communication and interpersonal skills, employers are sure to look your way!

Artificial intelligence

Without smart machines capable of handling tasks that normally need human thought, there is a chance that big data may not be fully optimised.

Simply put, artificial intelligence (AI) and big data complement each other, as smart machines can only improve according to the amount of data they’re fed, and big data is only useful if there’s software to analyse it. Increasing awareness of this issue has led to the emergence of AI engineering, which has grown exponentially over the past few years. In fact, Fortune Business Insight valued the AI market at US$27.23 billion in 2019, but expects it to hit US$266.92 billion by 2027!

This exponential growth comes as more and more companies are beginning to rely on AI for everyday business and tasks, as well as predictive and prescriptive maintenance of infrastructure. In addition, AI has proven useful in seemingly unexpected areas, according to Ann. “AI is now being used in digital print, so you can build a model of a factory, for instance, in a digital environment. So before you make any physical changes, you can carry it out in digital print to check out the performance before applying it physically,” he said.

Furthermore, AI itself is being further enable and optimised as well with graphics processing units (GPUs).

Knowledge of calculus and statistics will stand you in good stead if this is the area you wish to enter, as will a solid understanding of programming languages. Other technical skills to build would be a foundation in applied mathematics and algorithms, and signal processing techniques as well. On the non-technical side, communication skills are a plus, and employers will also lean towards candidates with strong domain knowledge.

Machine learning

Another up-and-coming area is machine learning (ML), a subset of AI. Defined as computer algorithms that automatically “learn” and improve through data, these machines are capable of thinking for themselves and boosting their accuracy over time without programming.

Known as a sequence of statistical processing steps in data science, in ML, algorithms pinpoint patterns and features scattered throughout massive amounts of data and make predictions and decisions off this data gleaned. As more data is processed, the more accurate the predictions and decisions become.

“Machine learning can make processes much faster and more effective and efficient,” Kok said. “At present, in the workforce, a lot of us use human judgement – use the naked eye to see what is good or not. But if it isn’t good, we then have to try and figure out where it’s gone wrong, which may take a lot of time.”

And as for growth, Forbes put the value of the ML market at US$1.58 billion in 2017, and Grand View Research has pegged its value at 2025 at a whopping US$96.7 billion!

Because machine learning engineers straddle the intersection between data science and software engineering, feeding data that scientists defined earlier into models and scaling them up in production-ready models, strong fundamentals in computer science and programming are vital. Knowledge in probability and statistics are goods skills to build too, as are those in data modelling and evaluation. An understanding of software engineering and system design is a must as well.

Other skills employers look out for

Companies often keep an eye out for fresh graduates with degrees in electrical, mechanical and computer engineering, taking them in not only for their academic backgrounds, but their new ideas as well.

“Graduates with semiconductor backgrounds and who have taken up courses in microelectronics will have an advantage over the competition too,” Kok said.

“Other transferable skills that are very useful in this industry are product management, sales, marketing and technical support skills,” Mr Chern Ann, EcoStructure Lead, Plant & Machinery, Schneider Electric, added.

As on how to develop these skills, Kok advised, “Look for internships when you’re in school. Even if it doesn’t work out for you, it’ll definitely be good experience!”